{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Abstraction\n",
    "\n",
    "It's important for finance companies to know risk of giving credit to their clients. Credit scores are essential for companies to decide on whether giving credit to someone or not. In this project, we experiment on features of clients to decide on which ones are more important. SVM, logistic regression and desicion trees are utilized and compared. An interface that calculates credit score for given features is provided."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intoduction\n",
    "&nbsp;&nbsp; Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, individuals and companies need access to credit. \n",
    "\n",
    "&nbsp;&nbsp; Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted. This competition requires participants to improve on the state of the art in credit scoring, by predicting the probability that somebody will experience financial distress in the next two years.\n",
    "\n",
    "# Data Dictionary\n",
    "| Variable Name                        | Description                                                                                                                                              | Type       |\n",
    "|--------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------|------------|\n",
    "| SeriousDlqin2yrs                     | Person experienced 90 days past due delinquency or worse                                                                                                 | Y/N        |\n",
    "| RevolvingUtilizationOfUnsecuredLines | Total balance on credit cards and personal lines of credit except real estate and no installment debt like car loans divided by the sum of credit limits | percentage |\n",
    "| age                                  | Age of borrower in years                                                                                                                                 | integer    |\n",
    "| NumberOfTime30-59DaysPastDueNotWorse | Number of times borrower has been 30-59 days past due but no worse in the last 2 years.                                                                  | integer    |\n",
    "| DebtRatio                            | Monthly debt payments, alimony,living costs divided by monthy gross income                                                                               | percentage |\n",
    "| MonthlyIncome                        | Monthly income                                                                                                                                           | real       |\n",
    "| NumberOfOpenCreditLinesAndLoans      | Number of Open loans (installment like car loan or mortgage) and Lines of credit (e.g. credit cards)                                                     | integer    |\n",
    "| NumberOfTimes90DaysLate              | Number of times borrower has been 90 days or more past due.                                                                                              | integer    |\n",
    "| NumberRealEstateLoansOrLines         | Number of mortgage and real estate loans including home equity lines of credit                                                                           | integer    |\n",
    "| NumberOfTime60-89DaysPastDueNotWorse | Number of times borrower has been 60-89 days past due but no worse in the last 2 years.                                                                  | integer    |\n",
    "| NumberOfDependents                   | Number of dependents in family excluding themselves (spouse, children etc.)                                                                              | integer    |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Related Work \n",
    "\n",
    "A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines\n",
    "http://www.sciencedirect.com/science/article/pii/S0957417404001782\n",
    "\n",
    "Benchmarking state-of-the-art classification algorithms for credit scoring\n",
    "https://link.springer.com/article/10.1057/palgrave.jors.2601545\n",
    "\n",
    "Using neural network ensembles for bankruptcy prediction and credit scoring\n",
    "http://www.sciencedirect.com/science/article/pii/S0957417407001558\n",
    "\n",
    "A comparative assessment of ensemble learning for credit scoring\n",
    "http://www.sciencedirect.com/science/article/pii/S095741741000552X\n",
    "\n",
    "Comprehensible credit scoring models using rule extraction from support vector machines\n",
    "http://www.sciencedirect.com/science/article/pii/S0377221706011878\n",
    "\n",
    "Neural network credit scoring models\n",
    "http://www.sciencedirect.com/science/article/pii/S0305054899001495\n",
    "\n",
    "Statistical Classification Methods in Consumer Credit Scoring: a Review\n",
    "http://onlinelibrary.wiley.com/doi/10.1111/j.1467-985X.1997.00078.x/full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from pandas import DataFrame\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Data Loaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "###\n",
    "### Load Data Set\n",
    "###\n",
    "data = pd.read_csv('cs-training.csv',sep=';').drop('Unnamed: 0', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# '-' in column name creates a problem when accessing\n",
    "Cols = []\n",
    "for i in range(len(data.columns)):\n",
    "    Cols.append(data.columns[i].replace('-', ''))\n",
    "data.columns = Cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#\n",
    "data = data.apply(lambda x: x.fillna(np.nanmedian(x),axis=0))\n",
    "# Drop rows with missing column data\n",
    "#data = data.dropna()\n",
    "\n",
    "###\n",
    "### Convert Data Into List Of Dict Records\n",
    "###\n",
    "data = data.to_dict(orient='records')\n",
    "\n",
    "###\n",
    "### Seperate Target and Outcome Features\n",
    "###\n",
    "\n",
    "vec = DictVectorizer()\n",
    "\n",
    "df_data = vec.fit_transform(data).toarray()\n",
    "feature_names = vec.get_feature_names()\n",
    "df_data = DataFrame(\n",
    "    df_data,\n",
    "    columns=feature_names)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Values\n",
    " There are some values in the dataset that are missing or having really awkward magnitudes. Therefore, firstly, we should take care of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "#col_mean = np.nanmean(X,axis=0)\n",
    "\n",
    "#Find indicies that you need to replace\n",
    "#inds = np.where(np.isnan(X))\n",
    "\n",
    "#Place column means in the indices. Align the arrays using take\n",
    "#X[inds]=np.take(col_mean,inds[1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization\n",
    "\n",
    "In this part we will show you the visualization of the data with respect to some aspects of them.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_freq(l):\n",
    "    ncount = l\n",
    "\n",
    "    ax2=ax.twinx()\n",
    "\n",
    "    ax2.yaxis.tick_left()\n",
    "    ax.yaxis.tick_right()\n",
    "\n",
    "    ax.yaxis.set_label_position('right')\n",
    "    ax2.yaxis.set_label_position('left')\n",
    "\n",
    "    ax2.set_ylabel('Frequency [%]')\n",
    "\n",
    "    for p in ax.patches:\n",
    "        x=p.get_bbox().get_points()[:,0]\n",
    "        y=p.get_bbox().get_points()[1,1]\n",
    "        ax.annotate('{:.1f}%'.format(100.*y/ncount), (x.mean(), y), \n",
    "                ha='center', va='bottom')\n",
    "\n",
    "    ax2.set_ylim(0,100)\n",
    "    ax2.grid(None)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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M/LMIeRhHE35RlwAd3P3z6FPEm4Ttcs4lJKweAdzn7oNKe1ZeXn6tSgU0fPiDDB/+IAcd\ndAi9ep3B6tW/8dBD9/Pxxx+Rn7+IoUOHs+uuuwOwaNEi3n33bVauXMm4cWP44gtn8OAb6NbtwDKf\nk+TalStXMmfObFq2bEnz5i1YtmwZJ554FAMGXMyBB/bgxRdf4NFHh1NQUEDPnkdwxhm91ZoUqSJa\ntsyplTOkq1RwjMoOJ8xe2p6QMPri1G1uoinB9xMy5OQDwwlr60rdg662BcfVq1dzzz23M27cGFav\nDr+azp27sttuezB06D08/vg4tt562/WuKyhYTq9eJ7Fq1SomTHihXM9Meu3IkY8wefIkRox4gi+/\nnMtpp53MgAGX0LbtVgwadCV9+57HkUfGN6AQkUyorcExo92q7t6tmLIXCAmSS7rmR9bfukdi6tSp\nw/nnX8Spp57Ot99+S6tWrdhiiy154IF7qVu3Lq1axYdug+zsBuy3X1fGjXuChQsX0qxZs2LP29Br\nly5dyujRj3HxxX8nKyuL1157mTZt2nLccScC0L37gbzyymQFRxHJKPVd1VD//veLvPfeTDbbrDl7\n7NGOLbYIwfDDD99np51+z48/zuP4449gwoSx6127dOkSsrKyqF9/k2Lv/fXXX23wtePGPUGLFi3X\ndLsuWPALubm5a443bdqMn39Oy8xsEZHEFBxrqCeffJzbb7+FVavWJp15881pfPTRBxx77Am0adOW\nxYsX88wz41m5cu0ewz/+OI8pU16lXbsONGrUuNh7b+i1S5Ys5oknRnHmmX3WTBJq3rwF8+fPp6h7\n/4cfvqdFi80r5HcgIrKh6g4aNCjTdagUS5euGJTpOlSmJk2aMGHCWD777FOWL1/O66+/yt1330bH\njnvTt+951KtXj5YtN+fpp8czc+YMVqxYwTvvvMnNNw+hsHA1Q4bcTG7uZgB8//13vPXWNBo2bEiT\nJk2oU6dO4mtTjRo1gp9/zuOCCy5aExxzcprw+OOPMn/+j8ydO5tnnpnAn/98Gma/r9Tfl4gUr3Hj\n7Fq5sUOt2c+xtk3IAXj55ZcYOXIE3333Dbm5zenR41B69TqDBg3WJhN65ZV/M2rUCL78cg4NGjSk\nY8e96NOnH1tvvc2acyZOfI4bbhjMFVdcQ8+eR5Tr2iKLFy/m+OOP4IorrmH//butc+yFF57l4Ycf\nYvny5Rx55DGcddY5mq0qUkXU1gk5Co4iIlKi2hoc9fFcREQkRsFRREQkpqqlj6vSbpr5VKarIFXQ\n5XtqTaZITaOWo4iISIyCo4iISIyCo4iISIyCo4iISIyCo4iISIyCo4iISIyCo4iISIyCo4iISIyC\no4iISIyCo4iISIyCo4iISIyCo4iISIyCo4iISIyCo4iISIyCo4iISIyCo4iISIyCo4iISIyCo4iI\nSEy9TFdARESqNzPrBNzs7t3MrB1wN/AbUAD8xd3nm9mdQBcgP7rsKGAFMBLYPCo/zd3zzGwf4E5g\nFTDZ3QdHz7kGODwqH+DuM9L1Tmo5iojIBjOzS4FhQIOo6E7gPHfvBkwALovKOwKHuHu36L9fgb7A\nx+7eFXgUGBidOxQ4hRBMO5lZezPrABwAdAJOBu5N53spOIqIyMaYAxyb8vPJ7v5B9H09YLmZ1QF2\nBB40s+lmdmZ0vAvwYvT9JOAgM2sCZLv7HHcvBF4CDorOnezuhe7+DVDPzFqm66VqTbdqbm4j6tWr\nm+lqSA3UsmVOpqsgkjHuPt7Mtk35eR6Ame0HnAvsDzQmdLXeBtQFXjOzmUAT4Nfo0nygaVS2KOUR\n+cB2wHLgf7HypkBehb8UtSg4LliwNNNVkBoqLy+/7JNEqqkN+fBnZicBVwKHR2OIdYE73X1pdPxV\nYA9CECx6QA6wMFaWWr6ihPK0qHLB0cwaAzcBxwGNgLeAi9z9s+h4D+AWwIAvgMvcfVKGqisiIinM\n7FTgbKCbu/8SFe8EjDGz9oThvC7ACMJEnJ7ADOAwYKq7LzKzFWa2PTAXOAQYTJiEc4uZ3Qq0Beq4\n+8/peo8qFxwJg7mdgROAX4AbgBfNbCdC0/pZ4DpgPPBn4Gkz6+Dun2aoviIiAkQtxLuAb4AJZgbw\nurtfY2aPAW8DK4FH3f1TM/sSGGFm0wgtw1OiW50DjCJ0wU5293ei+08lNJjqAP3T+S5ZhYWF6bx/\nuZnZz8Bgd787+nkX4FPCTKezAYtmQRWd/xrwhbv3Ke2+eXn5G/2iN818amNvITXQ5Xsek+kqiKRN\ny5Y5WZmuQyZUxdmqecBJZra5mdUH/gosIDSvuwJTYudPicpFREQqRFUMjn2ArYD5wFKgN9DT3RcS\n+pm/j53/Q3S+iIhIhaiKwXEH4EdCFoTOhDUu48ysLWGCzvLY+QWsXXwqIiKy0arUhBwz+x3wENDF\n3d+Oyk4BPgf+BiwDsmOXZQNLKrOeIiJSs1W1luOehNlJM4sK3H0l8D6hRfktsGXsmtas39UqIiKy\nwapacPwu+rp7UYGZZQG7ENY0TiPk1kvVHXijUmonIiK1QpXqViUsBH0beMTM+gE/AwOArQmph5oA\ns8xsMDCasCamEyF5rYiISIWoUi1Hd/8NOAJ4B3iCECh3ALq6+9fu/jFwDHA88AFwJHCEu3+eoSqL\niEgNVNVajkTpgHqXcvwF4IXKq5GIiNQ2VarlKCIiUhUoOIqIiMQoOIqIiMQoOIqIiMQoOIqIiMQo\nOIqIiMQoOIqIiMSUuM7RzCZv4D0L3f2QDbxWREQk40pLAnAQIeH3onLcrwnQbqNqJCIikmFlZcjp\n6+4zkt7MzPYB3ty4KomIiGRWaWOOg1m7S0ZS30bXiYiIVFslthzdvcQgZ2ZtCfsqznf3b1Ku+R4F\nRxERqebKlXg8CopjgH2jokIzex84xd3/W9GVExERyYTyLuW4i7Cd1BZAfWA7wibEj1RstURERDKn\nxOBoZueYWfz4zsAwd//J3Ve5+9fASOD36aykiIhIZSqtW/VMYICZXeHuE6KyZ4FxZvYI8D9CC7IP\nMC6ttRQREalEpU3I2dvMTgJuMrNLgEvc/TIz+xY4HmgFzCd0td5dKbUVERGpBKVOyHH3MWY2Hjgb\nGGtmM4HL3f2eSqmdiIhIBpQ5IScaW7wX2AGYCUwzs2Fm1jrttRMREcmAUluOZvYH4ACgLjDN3Qeb\n2X3A1cCnZjYUuMndf01/VUVEpCoys07Aze7ezcx2IKxgKAQ+Afq7+2oz603ohVwFDHH3582sIWFS\n5+ZAPnCau+dF2dbujM6dXLTu3syuAQ6PygeUJ4NbeZU2W7UP8AHQG+gFTDezy909z93PAzoC2wCz\nzezCdFVQRESqLjO7FBgGNIiKbgMGuntXIAs4ysy2AM4HOgOHADeaWTbQF/g4OvdRYGB0j6HAKUAX\noJOZtTezDoTGWifgZODedL5Xad2qAwkRv7277x1V8mozqwvg7nPd/RTCix6azkqKiEiVNQc4NuXn\njsDr0feTCJtY7A1Md/eCqKdxNrA7Ia68mHqumTUBst19jrsXAi9F9+hCaEUWRpnZ6plZy3S9VGnd\nqvUIzeJUdQifBNZw9/eAHhVcrwqXm9uIevXqZroaUgO1bJmT6SqIZIy7jzezbVOKsqKgBqGrtClh\nx6bU4bfiylPLFsXO3Q5YTlhCGL9HXoW8SExpwfEmYKiZ9QeWAXsQxhdXpaMi6bZgwdJMV0FqqLy8\n/ExXQSRtNuDD3+qU73OAhYRgl1NGeVnnriihPC1K7FZ197sIezM+DIwGurn7oHRVREREaoT3zaxb\n9P1hwFRgBtDVzBqYWVNCtrVPgOlAz9Rz3X0RsMLMtjezLMLQ3dTo3EPMrI6ZbQ3Ucfef0/USJbYc\nzWw/4EN3/zjpzcxsU2B3d9eejiIitdNFwENmVh/4HBjn7r+Z2V2EIFcHuNLdl5vZ/cAIM5tGaBme\nEt3jHGAUYaXEZHd/B8DMpgJvRffon86XyCosjA8rBmb2G7CPu7+b9GbRdN433b3KDe7l5eUX/6Ll\ncNPMpyqiKlLDXL7nMZmugkjatGyZk1X2WTVPaWOOWcDpZnZwOe7XdiPrIyIiknFl7efYdwPuudEt\nNBERkUwqLfF4efd6FBERqTLMrDOwG2FiaSd3fyPptQqAIiJS45jZBcAQ4EJgU+ABM7s46fUKjiIi\nUhOdTlgGssTd/wfsRdinOJGyxhwzwszOAi4FtgI+I+wl+Wp0rAdwC2DAF8Bl7j4pU3UVEZEq6Td3\nX2FmRT8vB35LenGVazma2WmEhLI3EfqKXweeNbNtzWwX4FlgLNAeeAZ4Oto9REREpMjrZnYr0NjM\njibEjleTXpwoOJpZpQTRKBvCYMLWJ8PdfTZwMSFJ7X7ABcDb7n69u//H3a8C3ozKRUREilxC6F38\nEPgLMJEw/phI0m7Vb83sMWCEu39e7iomZ4RtsMYUFbj7akIaO8xsIPBk7JophO1LREREigwA7nH3\nB4oKzOxm4LIkFydtET5KSOvziZm9Y2ZnR/nxKtpO0ddmZvaqmf1kZm9EqewgJBn4PnbND4SxSRER\nkSJDgKlm1ialLHFSm0TB0d3/TmjRHQL8F7gVmGdmT5jZoVF3aEVoEn0dQdg881BCctpXzWxnoBFh\nUDVVAWs32RQREQFwwtyVN8ysa3kvTjyWGG0w+bK79wK2IEyT3QJ4gdDteq2ZbVneCsSsjL5e7+6P\nR3tF9if0G/clbJ2VHbsmG1iykc8VEZGapdDdnwKOAf4Vbb+4IunF5Z5oY2ZbAGcTBja7Al8BTxHG\n/b4ws+PLe88URV2ma3YCiTbN/Bz4HfAtEA/ArVm/q1VERGq3LAB3/4gwofMEovkrSSSdrdrIzE41\ns5cIAeo6Qvfqge6+vbufR5hMMw24q3z1X8d7hFbgXinPzgJ2AeZE9z8gdk13IHFKIBERqRWOLvom\n2vfxIOCMpBcnna36E9CQsGFlP+AJd19n+3N3LzSzt4Ddkz48zt2XmtntwPVmNp/QguwHbA8cB9QH\nZpnZYMIGzKcAndiwBOkiIlLDmNkgdx8EDDKz4jbCGJ3kPkmD433AwwmWcdwOXJ/wniW5GlgK3AFs\nDnwA9HB3BzCzYwgZci4D/gMckeblJSIiUn3Mir5O2ZiblLjZcZyZ7QF0d/c7op93A84HbqsOwUmb\nHUu6aLNjqcmq+2bHURKb9sAcd1+Y9LqkY44HAu8Ap6YUZxPG/2aY2d7lqKuIiEhamNkOZjbTzA43\ns2zgLWAc8FG0hVUiSWerDiHkMV0TBN19JvB7QkqemxPXXEREJH3uIqzFn0ho0G0K7EhYXXFL0psk\nDY67AQ9EqdzWiH5+COiY9IEiIiJp1Mbdn4iWAR4MjHP3Ve7+NZA4s1vS4PgrsEMJx7YhTKARERHJ\ntCxYswywO/Byys+Nk94kaXCcAAwxs0NTC6OxyOsISQBEREQy7SMzu4yw8qEAmG5m9YG/A28nvUnS\npRxXEBbmTzSz5UAe0IKQ0/RdEmY5FxERSbP+wI2E9KZHu/tqM7sD2Jly7OBUnqUcdYCeQBdgM0JX\n6zTgufhYZFWkpRySLlrKITVZdV/KsaGSthyLJt88H/0nIiJSYyUOjmbWHfgTYUAzPlZZ6O5nV2TF\nREREMiVRcDSzCwnrRorGG+PdqBvdZSkiIlJVJG05ng+MAv7q7on3wxIREckEM9sGGAZsC+xPiGFn\nuvtXSa5PGhxbAcMUGEVEJJWZnQ6cHv3YgLBn4r6E+SlfROX3u/sYM+tN2A94FTDE3Z83s4bASMJG\nE/nAae6eZ2b7AHdG505298HlrNoDwD+Am4AfCbtxPEoIlGVKus7xQ2DXclZMRERqOHd/xN27uXs3\nwo4Y5xOypt1WVB4Fxi2iY52BQ4Abo9ynfYGP3b0rIXgNjG49lLAtYRegk5m1L2fVWrj7ZCDL3Qvd\n/SGgSdKLk7Yc/waMNrN84E2KyYjj7j8kfaiIiNQsZrYn8Ad3729m94ciO4rQehxAyM093d0LgAIz\nm03Y/7cLa3OeTgKuMrMmQLa7z4nu/RJhs+L3y1GlZWbWlmhOjJl1ISQFSCRpcHwV2AR4hJIn39RN\n+tBMyM1tRL16VbqKUk21bJmT6SqIVAVXAEVdnzMIQ3GzzOxK4BrC3ry/ppyfT8h12iSlPLVsUezc\n7cpZnwvqB/NAAAAW40lEQVQJXbvbm9kHhPX5Jya9OGlwPKeclapyFixQ+ldJj7y8/ExXQSRtknz4\nM7NmgLn7a1HRUyl7Jz4F3A28AaTeLAdYSAiCOaWUpZYn5u7vmtlewE6Extt/yjNvJlFwdPcR5amU\niIjUKvsDr6T8/JKZnefuM4ADCWORM4DrzawBYT/gnYFPgOmE7GszgMOAqe6+yMxWmNn2wFzCGGWi\nCTlm9jAl9HCaGe5+ZpL7lCcJQB3gJMIWIFsSBlb3AWa5+2dJ7yMiIjWOEYJYkb7A3Wa2kjBTtE8U\n8O4CphImg17p7suj8ckRZjYNWEGYhAOhx3IUodU32d3fSViXKRv9NiTMrWpmTYEXCQOqXxO2qdoL\nuAHYDzjA3cszUFrplFtV0kW5VaUmq865Vc2sHfBH1i4H+U/Sa5Mu5fgHsDXQntB/W/TLOgH4FBiS\nuLYiIiJpZmYXAWOB1sDvgOfM7Iyk1ycNjscAV7j7R6T05bp7PmGBZafENRYREUm/s4GO7n6xu/+N\n0PN5SdKLkwbHRsBPJRxbTsiKICIiUlX8AqxM+XkxYUlIIkkn5MwkDLBOKubYycB7SR8oIiJSCeYA\nb5nZaMKY4zHAIjO7GsDdry3t4qTB8Srg32Y2C3iB0LV6opkNBI4ADt3AyouIiKTDf6P/ino2/x19\nTTTBKOk6xzfM7GDgRkIWhCxC3+37wBHu/kpp14uIiFSmDUhUvo7E6xzd/Q2gc5RBPRdY5O6LN+bh\nIiIi6WBmFxDS1jWNirKAQndPlEc06WbHrYspbhIlhwWUeFxERKqUvwHt3P2bDbk4acvxO0pOOF5E\nWb1FRKSq+AyYv6EXJw2OZ7J+cNwU6Ap0j46LiIhUFXcBH5vZ24TZqgAVm1vV3R8p4dC9ZnYb8GfC\nLFYREZGq4C5gJCHlabklnpBTimeBZyrgPiIiIhVleVlrGUtTEcGxE+tmIagwZrYPMA04yN2nRGU9\nCLtGG2GH6cvcvbjkBCIiUnu9bGb/JCSvWbOPY7TyokxJZ6s+WExxXWArQsbzYUnuUx5m1hh4jJSJ\nPma2C6Gleh0wntCd+7SZdXD3Tyu6DiIiUm21j752SCkrJMSsMiVtOfZg/Qk5hYTdmm8ibF1V0W4j\nzJLdIaXsAuBtd78++vkqM+sSlfdJQx1ERKQacvfuG3N90gk5227MQ8rLzHoChxN2hf4o5VBX4MnY\n6VMI+V1FREQAiBpOlxBWVmQReiG3SRrPku7KUWnMrAXwL+AsYEHscFvg+1jZD4TuXRERkSLDgKcJ\njcB7CXNUEu9Yn3TMcSVlJwEoUuju2UkrUIwHgGfd/UUzaxs71oiwRVaqArRlloiIrGuZuz9sZtsS\nGlq9gVlJL0465ng+MAT4GXicMBbYHDgS2Be4Lzq2UczsNMIg6u4lnLIMiAfebGDJxj5bRERqlOVm\nthngwD7u/mo00TORpMFxH+Bt4Eh3/y2l/BYzGwG0cvfzEle5ZKcTuk5/NDNYu7XIpOg53wJbxq5p\nzfpdrSIiUrvdBowBjgXeNbM/E/YmTiTpmOMxwD2xwFhkFNAz6QPLcCqwC9Au+u+QqPws4GrCmscD\nYtd0BxKtWxERkdrB3ccCPdw9H+hIiC+9kl6ftOW4FNi+hGPtWX/izAZx93VagGZWNL74vbv/ZGZ3\nA7PMbDAwGjiFkISgb0U8X0REqj8z+xPwmbvPNbOjgb8S9h/+GFid5B5Jg+MTwPVRsHoWyAO2AE4i\n7JeVjnWO63H3j83sGEKGnMuA/xA2W/68Mp4vIiJVm5ldTIhNp5nZ7oTezQsIvZK3AgOS3CdpcLyM\nsFziQcJs0lRD3X1IwvuUi7t/x9pxx6KyF1CScxERKV4vYF93X2pmNxFWPwwzsyzCNlaJJE0CUAAc\nZ2a7Ehbi5xJmp77q7rPLX3cREZG0KHT3pdH33QmrKXD3wmiiZyLlSjzu7p+Y2X+AFsDP7r6qrGtE\nREQq0Soza0bIjNMemAxgZtuQsq9jWRJnyDGzjmb2EpBPWOe4u5k9YmZXlavaIiIi6XMT8AFh+eEw\nd59nZicCrxDmqySSKDia2X6EZRSbATezdhzwW2CQmWm2qIiIZJy7jwP2A3q6e7+oeDFwlrs/lvQ+\nSbtVbwb+7e5Hmlk9wppD3P0qM2sE9APuT1x7ERGRNHH3Hwh5t4t+nljeeyQNjh2B46Lv4zlWnwPO\nKe+DRUSkZjCz9whbGAJ8CVwPPEKIF58A/d19tZn1Bs4mjP0NcffnzawhMBLYnDBsd5q750Wb3d8Z\nnTvZ3QdX5jslHXPMB1qVcKxNdFxERGoZM2sAZLl7t+i/Mwip2wa6e1fCMNxRZrYFIU93Z0L2sxvN\nLJuQxOXj6NxHgYHRrYcSEr10ATqZWXsqUdKW47PAEDP7kLX7KxZGL3sF1WDdYW5uI+rVq5vpakgN\n1LJlTqarIJJJewCNzGwyIaZcQehtfD06PgnoAfwGTI+WBhaY2WzCJhNdWDtRZhJhE/smQLa7zwGI\nJoMeRMhyUynKkwRgL+Bd1ib5fgzYhtCve3nFV61iLViwtOyTRDZAXp46TqTmSvDhbykh88wwYEdC\ngMty96IhuHygKdAE+DXluuLKU8sWxc7dboNfYgMk6lZ1919Ym8P0TeBl4HPg70AHd89LWw1FRKQq\n+y8w0t0L3f2/wP9YdxguB1hICHY5ZZSXdW6lSbrZ8d3ACHd/CHgovVUSEZFq5ExgN6CfmbUmtPom\nm1k3d58CHAa8Bswg5OhuQNiHd2fCZJ3phJ2dZkTnTnX3RWa2wsy2B+YSxigrdUJO0m7VvxLGHUVE\nRFL9C3jEzKYRZqeeSUgv+pCZ1Sf0Mo5z99/M7C5gKqHX8kp3X25m9wMjoutXECbhQFgFMQqoS5it\n+k5lvlRWYWF8Zcb6zOxVYJq7X53+KqVHXl5+2S9ahptmPlURVZEa5vI9j8l0FUTSpmXLnKyyz6p5\nkrYc3wMuM7PjCWl5FseOF7r72RVaMxERkQxJGhyPI8xKbQjsW8zxjW6ViYiIVBVJt6z6XborIiIi\nUlWUuJTDzP5oZptWZmVERESqgtLWOf4b2CW1wMz6mFnz9FZJREQks0oLjuvMUDKzuoSdN7ZJa41E\nREQyLPFmx5FaOaVXRERql/IGRxERkRpPwVFERCSmrOBY3PpFrWkUEZEarax1juPMrCBW9nQxZYXu\nbhVYLxERkYwpLTiOKKZseroqIiIiUlWUGBzd/YzKrIiIiEhVoQk5IiIiMQqOIiIiMQqOIiIiMQqO\nIiIiMQqOIiIiMQqOIiIiMYk2O65MZtYKuAXoATQE3gEucvdPouM9ouMGfAFc5u6TMlRdERGpgapU\ny9HM6gBPATsBRwH7Ab8Cr5hZczPbBXgWGAu0B54hZOz5Q4aqLCIiNVBVaznuAewL7OLunwOYWS/g\nF+BwoDPwtrtfH51/lZl1AS4A+mSgviIiUgNVqZYj8A3wJ8BTylZHX3OBrsCU2DVTonIREZEKUaVa\nju7+P+CFWPH5hLHHycB1wPex4z8AW6W/diIiUltUtZbjOszsSOBG4Laom7URsDx2WgHQoLLrJiIi\nNVeVDY5mdjowHhgDXBoVLwOyY6dmA0sqr2YiIlLTVcngaGZXAg8DQ4G/uHvRuOO3wJax01uzfler\niIjIBqtywdHMLgWGAFe7+3nuXphyeBpwQOyS7sAblVU/ERGp+arUhBwz2x24ARgOPGRmW6Qczgfu\nBmaZ2WBgNHAK0AnoW9l1FRGp7cxsE8K/19sShriGEHr4nickaQG4393HmFlv4GxgFTDE3Z83s4bA\nSGBzwr/xp7l7npntA9wZnTvZ3QdX4msBVa/leDJQFzgTmBf772/u/jFwDHA88AFwJHBE0ZpIERGp\nVKcC/3P3rsChwD1AR8Ikym7Rf2Oihs75hLXqhwA3mlk2oWHzcXT9o8DA6L5DCY2fLkAnM2tfqW9F\nFWs5uvsVwBVlnPMC6y/3EBGRyjcWGBd9n0Vo6XUEzMyOIrQeBwB7A9PdvQAoMLPZwO6E4HdLdP0k\nQmKXJkC2u88h3Ogl4CDg/cp5paBKBcd0ys1tRL16dTNdDamBWrbMyXQVRDLC3RcDmFkOIUgOJHSv\nDnP3WdHkymsIPX2/plyaDzQFmqSUp5Ytip27XRpfo1i1JjguWLA001WQGiovLz/TVRBJm7I+/JnZ\nVoSc2Pe5++Nm1szdF0aHnyLMFXkDSL1RDrCQEARzSilLLa9UVW3MUUREqoloF6XJhN2RhkfFL5nZ\n3tH3BwKzgBlAVzNrYGZNgZ2BT4DpQM/o3MOAqe6+CFhhZtubWRZhjHJq5bzRWrWm5SgiIhXuCkLe\n66vM7Kqo7ELgdjNbCfwI9HH3RWZ2FyHI1QGudPflZnY/MMLMpgErCJNwAM4BRhEmaE5293cq75WC\nrMLCwrLPqgHy8vI3+kVvmvlURVRFapjL9zwm01WoMRYsWMCDD97LtGlvUFBQwE47GWef3Z/ddtuj\n2PPnzfuBE044stR73nXXUDp02JOlS5dyww2DeOut6bRtuzUXXngZe+zRbp1zn3pqHGPGjGLkyLHU\nq6e2A0DLljlZma5DJuhPX0SqhKVLl3Duub35+ec8TjzxFHJymjBhwpNccEE/HnpoBNtvv8N61zRr\nlstVV127XnlBQQF33PEPmjXLZYcddgLgscceZubMGfTu3Zf335/F5ZdfyJNPPkNOThjeWrFiBY89\n9jB9+vRTYBQFRxGpGkaOHME333zN3Xc/QLt2HQA48MCDOfHEo3j88RFcddV1613TsGFDDjmk53rl\nd975T1atWsU11wyhSZMmALzyymSOPvp4Tj75VI488hh69jyQt96aTo8ehwLw7LMTyM7O5uCDD03j\nW0p1oeAoIhlXWFjIpEnPs+++XdYERoDmzVvQv/+AcrXk5syZzfjxYzjssD+xxx5r147//HMeW27Z\nGoBGjRrTtGkz8vLmA6GlOXLkCPr1O5+6dbXkSxQcRaQKmDfvB/LyfuKUU/4ChGC5bNkyGjVqxLHH\nnlCuez344L1kZ2fTu3e/dcqbNm3GkiWLAVi9ejVLliymadNmADzzzHgaN27MQQcdUgFvIzWBlnKI\nSMZ99923AOTm5nLvvXdy6KHd6NFjf0466WimTUu+r8Ds2V8wffpUjjrqOFq0aLHOsXbtOjBx4nN8\n+eVcxox5nJUrV9K+fUcKCpYzatQITj/9LOrU0T+JEuhvgohkXH5+SKQwbNhQ3nprGhdccDEDBw6m\nQYMGXHHFxbz7brKZ/E8/PY66dety/PEnrXesd+++rFy5kl69TuT++++if/8LaNOmLRMmjCMnpwkH\nHtijQt9Jqjd1q4pIxq1cuQKAxYvzefzxCWsm0XTuHFqPDzxwL3vt1anUexQULOellybRufP+bLFF\nfNtXaN26DSNHjmXOnNm0bNmS5s1bsGzZMh5//FEGDLiYOnXq8OKLL/Doo8MpKCigZ88jOOOM3mpN\n1lL6UxeRjGvYsCEA++/ffU1gBMjJyaFLl/1x/5ylS0tPAfneezNZtmwp3bsfWOI5m2yyCb///c40\nbx66XMePH0Nubi5//OPBzJ07h+uvH8Rxx53EpZdeybhxY3j++Wcq4O2kOlJwFJGMa9FicwByczdb\n71izZrnRBJ3Sg+Nbb02nfv367Ldfl0TPXLp0KaNHP8YZZ/QmKyuL1157mTZt2nLccSfSqdO+dO9+\nIK+8Mrn8LyM1goKjiGTcdtttT/369fnyy7nrHZs37wfq18+mWbPcUu/x8ccfYrYzjRtvmuiZ48Y9\nQYsWLenWLbQ0Fyz4hdzctc9o2rQZP/+cV463kJpEwVFEMq5hw4Z07rw/b745lblz56wp/+GH75k+\n/Q26dt2/1PWHq1at4quvvmSnnSzR85YsWcwTT4zizDP7kJUVsqM1b96C+fPnU5RS84cfvl/TopXa\nRxNyRKRK6NfvfN5/fxbnn38OJ5xwMptssgljxz5B/frZ9OnTH4Dvv/+OTz75iF133Z02bdquuXb+\n/B9ZuXIlrVptkehZTz45ms03b8X++3dfU9a1azeGD3+Qm266jtat2/DGG69x4YWXVexLSrWhlqOI\nVAlbbtmaBx54mHbtOjB69GOMGPEvdtxxJ4YOHb4mEH744ftcd93VfPjhupvC//pr2O6vUaPGZT5n\n8eLFjBnz+DqtRoAddtiRyy+/ilmz3mXs2Cf4v//rxeGHl57UXGou7cpRDtqVQ4qjXTmkJqutu3Ko\n5SgiIhKj4CgiIhKjCTkiNUDhuPW3cxLJOv6qTFeh2lLLUUREJEbBUUREJEbBUUREJEbBUUREJEbB\nUUREJEbBUUREJEbBUUREJEbBUUREJEbBUUREJKZaZsgxs7rAEOB0IAd4Eejv7vMzWS8REakZqmvL\ncRBwGvAXYH+gLTA+kxUSEZGao9q1HM2sPnABcL67/zsqOxn40sz2c/c3M1pBEZFawszqAPcBewAF\nwFnuPjuztaoY1bHl2I7QlTqlqMDdvwK+ArpmpEYiIrXT0UADd98XuBz4Z4brU2GqY3BsG339Plb+\nA7BVJddFRKQ260KY84G7vw3smdnqVJxq160KNAJWu/vKWHkB0KCkiypiN+t/HvaXjb2FSHr0vSXT\nNZDaqQnwa8rPv5lZPXdflakKVZTq2HJcBtQxs3hgzwaWZKA+IiK11SLCMFeROjUhMEL1DI7fRl+3\njJW3Zv2uVhERSZ/pQE8AM9sH+Diz1ak41TE4fgjkAwcUFZjZtsC2wBuZqZKISK30FLDczN4Ebgf+\nluH6VJiswsLCTNeh3MzsJkICgNOBnwhTiZe7e7fM1UpERGqK6jghB2AgsAkwMvr6ItA/ozUSEZEa\no1q2HEVERNKpOo45ioiIpFV17VaVSlBWaigzOwK4GlgFDHf3hzJSUanVzKwTcHN8zoH+fsrGUMtR\nSlNiaigz24QwO60HYeZwHzNrlZFaSq1lZpcCw4glANHfT9lYCo5SmtJSQ+0MzHb3Be6+AphG2CFF\npDLNAY4tplx/P2WjKDhKaYpNDVXCsXygaWVVTATA3ccD8VSSoL+fspEUHKU0paWGih/LARZWVsVE\nyqC/n7JRNCFHSjMdOAJ4spjUUJ8DO5rZZsBiQpfVrZVfRZFi6e+nbBQFRynNU8DBUWqoLOAMMzsF\n2NTdHzSzC4GXCD0Qw91duW0lo/T3UyqKkgCIiIjEaMxRREQkRsFRREQkRsFRREQkRsFRREQkRsFR\nREQkRks5RNLAzJoANxLyeq4CFgAXETK3DNLG3CJVm1qOIhUs2s1kIvAL0M7d2wHXApOA5pmsm4gk\no5ajSMXrDrQGrnH31QDu/pqZnQFsWnSSmR0AXA80AnKBS919bLSQ/VLgN+BL4FSgBTAKaAysBs6P\nksGLSBqo5ShS8doD7xYFxiLuPhH4KaXoPMIemR2AvxL2HgQYAvRw947Af4DfR8efd/c9CYGzS3pf\nQaR2U8tRpOKtJqTbK8upwJ/M7ARgH9a2Kp8DppvZ08B4d//AzBoDE8ysPfACcE8a6i0iEbUcRSre\nTKCDma0TIM3sBtYNmlOBvYFZhO7VLAB3vwA4jjBmOdLMTnX36cAuhFyhJxECqIikiYKjSMWbSug+\nvcbM6gKY2SHAGcDm0c+bATsBV0fdrT2AumZWz8y+AH529xuBR4H2ZnYL0MvdRwDnAh0q+6VEahMl\nHhdJAzNrAdwO7EnYjPdnwlKOpkRLOczsn8DRhL0H3yK0CLcGjgSuApYS9iA8jfBB9nHCvoS/ATe7\n+5OV+U4itYmCo4iISIy6VUVERGIUHEVERGIUHEVERGIUHEVERGIUHEVERGIUHEVERGIUHEVERGL+\nHx0cNoTMfod4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1350fcd4c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x = df_data.SeriousDlqin2yrs , palette=\"Set2\")\n",
    "sns.set(font_scale=1.5)\n",
    "ax.set_ylim(top = len(data))\n",
    "ax.set_xlabel('Class')\n",
    "ax.set_ylabel('Sample Size')\n",
    "plt.title('Class Frequencies')\n",
    "\n",
    "plot_freq(l = len(df_data.SeriousDlqin2yrs))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import  linear_model\n",
    "#clf = linear_model.LogisticRegression(C=1e5)\n",
    "#clf.fit(X_Train,Y_Train)\n",
    "#clf.predict_proba(X[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#clf.coef_\n",
    "#ind = np.where(Y_test == 1)\n",
    "#ss = clf.predict_log_proba(X_test[ind]) \n",
    "#t = ss.argmax(axis = 1)\n",
    "#a = np.intersect1d(ind,np.where(t == 1))\n",
    "#100*len(a)/len(t)\n",
    "#print(clf.predict_proba(X_test[37441].reshape(1,-1)))\n",
    "#y_test[37441]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outlier detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "We are going to examine some features to eliminate outliers. For instance, in *RevolvingUtilizationOfUnsecuredLines*, there are some values that are close to 50000. We should eliminate such values to prevent our error rate being very high. \n",
    "\n",
    "### RevolvingUtilizationOfUnsecuredLines Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median: 0.1541807 \n",
      "Mean: 6.0484381\n",
      "Values less than 2 : 149629 in 150000. Ratio: 99.75267%\n"
     ]
    },
    {
     "data": {
      "image/png": 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DDzjl7zxJHw38Xp4uPNzv+ICq1dnEy1mxWNENN0jz55vXvSz0nocR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\nQgghhBBCCCGEqPH/VGsSbT9COTUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c4d7881358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.plot(data2.RevolvingUtilizationOfUnsecuredLines)\n",
    "plt.figure(figsize=(20,15))\n",
    "ax = plt.subplot(211)\n",
    "#ax.set_ylim(0,20)\n",
    "plt.plot(df_data.RevolvingUtilizationOfUnsecuredLines, 'bo',df_data.RevolvingUtilizationOfUnsecuredLines, 'k')\n",
    "print('Median: %.7f \\nMean: %.7f' %(np.median(df_data.RevolvingUtilizationOfUnsecuredLines),np.mean(df_data.RevolvingUtilizationOfUnsecuredLines)))\n",
    "ruoelLt2=len(df_data[df_data.RevolvingUtilizationOfUnsecuredLines < 2])\n",
    "ruoelACt=len(df_data.RevolvingUtilizationOfUnsecuredLines)\n",
    "print('Values less than 2 : %d in %d. Ratio: %.5f%%' %(ruoelLt2,ruoelACt,100*ruoelLt2/ruoelACt))\n",
    "#sns.kdeplot(data2.RevolvingUtilizationOfUnsecuredLines, shade=True, color=\"r\")\n",
    "#data2.age.plot.box()\n",
    "#data2.RevolvingUtilizationOfUnsecuredLines.plot.box()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Therefore we need to clean outliers by considering the ratio we obtained above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ind = np.where(df_data.RevolvingUtilizationOfUnsecuredLines>2)\n",
    "df_data.RevolvingUtilizationOfUnsecuredLines[ind[0]] = 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0.0: 1,\n",
       "         21.0: 183,\n",
       "         22.0: 434,\n",
       "         23.0: 641,\n",
       "         24.0: 816,\n",
       "         25.0: 953,\n",
       "         26.0: 1193,\n",
       "         27.0: 1338,\n",
       "         28.0: 1560,\n",
       "         29.0: 1702,\n",
       "         30.0: 1937,\n",
       "         31.0: 2038,\n",
       "         32.0: 2050,\n",
       "         33.0: 2239,\n",
       "         34.0: 2155,\n",
       "         35.0: 2246,\n",
       "         36.0: 2379,\n",
       "         37.0: 2521,\n",
       "         38.0: 2631,\n",
       "         39.0: 2987,\n",
       "         40.0: 3093,\n",
       "         41.0: 3122,\n",
       "         42.0: 3082,\n",
       "         43.0: 3208,\n",
       "         44.0: 3294,\n",
       "         45.0: 3502,\n",
       "         46.0: 3714,\n",
       "         47.0: 3719,\n",
       "         48.0: 3806,\n",
       "         49.0: 3837,\n",
       "         50.0: 3753,\n",
       "         51.0: 3627,\n",
       "         52.0: 3609,\n",
       "         53.0: 3648,\n",
       "         54.0: 3561,\n",
       "         55.0: 3416,\n",
       "         56.0: 3589,\n",
       "         57.0: 3375,\n",
       "         58.0: 3443,\n",
       "         59.0: 3280,\n",
       "         60.0: 3258,\n",
       "         61.0: 3522,\n",
       "         62.0: 3568,\n",
       "         63.0: 3719,\n",
       "         64.0: 3058,\n",
       "         65.0: 2594,\n",
       "         66.0: 2494,\n",
       "         67.0: 2503,\n",
       "         68.0: 2235,\n",
       "         69.0: 1954,\n",
       "         70.0: 1777,\n",
       "         71.0: 1646,\n",
       "         72.0: 1649,\n",
       "         73.0: 1520,\n",
       "         74.0: 1451,\n",
       "         75.0: 1241,\n",
       "         76.0: 1183,\n",
       "         77.0: 1099,\n",
       "         78.0: 1054,\n",
       "         79.0: 981,\n",
       "         80.0: 876,\n",
       "         81.0: 774,\n",
       "         82.0: 647,\n",
       "         83.0: 512,\n",
       "         84.0: 480,\n",
       "         85.0: 483,\n",
       "         86.0: 407,\n",
       "         87.0: 357,\n",
       "         88.0: 313,\n",
       "         89.0: 276,\n",
       "         90.0: 198,\n",
       "         91.0: 154,\n",
       "         92.0: 93,\n",
       "         93.0: 87,\n",
       "         94.0: 47,\n",
       "         95.0: 45,\n",
       "         96.0: 18,\n",
       "         97.0: 17,\n",
       "         98.0: 6,\n",
       "         99.0: 9,\n",
       "         101.0: 3,\n",
       "         102.0: 3,\n",
       "         103.0: 3,\n",
       "         105.0: 1,\n",
       "         107.0: 1,\n",
       "         109.0: 2})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAD3CAYAAADi8sSvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqEtSIUZdkgox6pJUiFGXpEL+D2qzJfN5iAxhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c4e0c9dc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "plt.figure(1)\n",
    "df_data.age.plot.box()\n",
    "Counter(df_data.age)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fRDE3xsShh50ORy0CTfrqNXKZyrN1gcgKhyNRTuldfjX+0BJSY0+THP2N0+Go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\nNOkrpZSLaNJXSikX0aSvlFIu8v8BB/gTG3PKiHoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c4d78810f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(2)\n",
    "sns.set_color_codes()\n",
    "sns.distplot(df_data.age, color=\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ind = np.where(df_data.age<21)\n",
    "df_data.age[ind[0]] = 21.\n",
    "ind = np.where(df_data.age>94)\n",
    "df_data.age[ind[0]] = 94.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NumberOfTime30-59DaysPastDueNotWorse Varience\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0.0: 126018,\n",
       "         1.0: 16033,\n",
       "         2.0: 4598,\n",
       "         3.0: 1754,\n",
       "         4.0: 747,\n",
       "         5.0: 342,\n",
       "         6.0: 140,\n",
       "         7.0: 54,\n",
       "         8.0: 25,\n",
       "         9.0: 12,\n",
       "         10.0: 4,\n",
       "         11.0: 1,\n",
       "         12.0: 2,\n",
       "         13.0: 1,\n",
       "         96.0: 5,\n",
       "         98.0: 264})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(df_data.NumberOfTime3059DaysPastDueNotWorse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Set outlier values to median , that is 0.\n",
    "ind = np.where(df_data.NumberOfTime3059DaysPastDueNotWorse>95)\n",
    "df_data.NumberOfTime3059DaysPastDueNotWorse[ind[0]] = 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mad_based_outlier(points, thresh=3.5):\n",
    "    if len(points.shape) == 1:\n",
    "        points = points[:,None]\n",
    "    median = np.median(points, axis=0)\n",
    "    diff = np.sum((points - median)**2, axis=-1)\n",
    "    diff = np.sqrt(diff)\n",
    "    med_abs_deviation = np.median(diff)\n",
    "\n",
    "    modified_z_score = 0.6745 * diff / med_abs_deviation\n",
    "\n",
    "    return modified_z_score > thresh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DebtRatio Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median: 0.3665078 \n",
      "Mean: 353.0050758\n"
     ]
    },
    {
     "data": {
      "image/png": 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FhoBPxBhvCCGcAlwNnAYcBt4dY+wLIVwAfCm37M0xxo/nPu9jQHuu/EMxxrtLu7qSJEmS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\nCIIgCIIgCIIgTJr/DyZvThq6H29UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c4d8027a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "ax = plt.subplot(211)\n",
    "#ax.set_ylim(0,20)\n",
    "plt.plot(df_data.DebtRatio, 'bo',df_data.DebtRatio, 'k')\n",
    "print('Median: %.7f \\nMean: %.7f' %(np.median(df_data.DebtRatio),np.mean(df_data.DebtRatio)))\n",
    "#ruoelLt2=len(df_data[df_data.RevolvingUtilizationOfUnsecuredLines < 2])\n",
    "#ruoelACt=len(df_data.RevolvingUtilizationOfUnsecuredLines)\n",
    "#print('Values less than 2 : %d in %d. Ratio: %.5f%%' %(ruoelLt2,ruoelACt,100*ruoelLt2/ruoelACt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7/MPAD4CNJJexj4+Iv0i6GhgJ9AKmR8Q9ZS/eegRJo4DvAOuBm4ErSQJwnaRv\nAy9ExBz/Tlpb+J5cdv0fSU9s+Q8YDiyJiDHAgcDpzdofDjwNjAYuAwZKGgt8ICJGAIcBkyT9S9nO\nwHqivhExMiL+q9CH/p20tnJPLru2ulwpqQ74qqTDSGY26NOs/W3ARSRz160GLgH2AQ5IQxKgNzAU\n+E0pC7ceLbaxfctUfv6dtDZxT67nOAl4MyJOIJldvL+k/DlAxwELIuJfgXtIAu8F4PE0LD8F/AhY\nUc6ircfZnPd6HTA4/T39WLrNv5PWJu7J9Ry/AO6WdDDJWlEvAbvkff4r4HZJk0nudZwLLANGSVoA\nbA/MjYjG8pZtPdg1wEPAn4BV6bYH8O+ktYFnPDEzs8zy5UozM8ssh5yZmWWWQ87MzDLLIWdmZpnl\nkDMzs8xyyJmZWWY55MzMLLMccmZmlln/AyQAsKEJoEh/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c4e2ce3da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(mad_based_outlier(df_data.DebtRatio))\n",
    "plot_freq(l = len(df_data.DebtRatio))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "minUpperBound = min([val for (val, out) in zip(df_data.DebtRatio, mad_based_outlier(df_data.DebtRatio)) if out == True])\n",
    "### Set outlier values to upperbound, that is minUpperBound.\n",
    "ind = np.where(df_data.DebtRatio>minUpperBound)\n",
    "df_data.DebtRatio[ind[0]] = minUpperBound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150000.000000\n",
       "mean          0.607359\n",
       "std           0.588294\n",
       "min           0.000000\n",
       "25%           0.175074\n",
       "50%           0.366508\n",
       "75%           0.868254\n",
       "max           1.641791\n",
       "Name: DebtRatio, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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265EN9oguQVQ1RxelYqEt0vpiIU/ZQq1joVBrkS3l6SHpiXq09/jEtLWmmgkq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iimyQg2f1kwnwoU8Ou7NoBUb8P8fla259heVyTeTa9fOSf9HgxgplhyJb4QcBzsO3yqrkVPVLp9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c4e33fd588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "ax = plt.subplot(211)\n",
    "plt.plot(df_data.DebtRatio, 'o')\n",
    "\n",
    "df_data.DebtRatio.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MonthlyIncome Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median: 5400.0000000 \n",
      "Mean: 6418.4549200\n"
     ]
    },
    {
     "data": {
      "image/png": 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Qws2kw595wIdijMdCCJ8HvhpCeIj0CKXrM+t4D3AbMJ/0vEttACGEB4HWzDrel1n2psw6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\nMAkhhBBCCCGEEEKIYfn/W6oV7PNBRZQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c4e33d2828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "ax = plt.subplot(211)\n",
    "#ax.set_ylim(0,20)\n",
    "plt.plot(df_data.MonthlyIncome, 'bo',df_data.MonthlyIncome, 'k')\n",
    "print('Median: %.7f \\nMean: %.7f' %(np.median(df_data.MonthlyIncome),np.mean(df_data.MonthlyIncome)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150000.000000\n",
       "mean       5993.580100\n",
       "std        3164.949126\n",
       "min        1500.000000\n",
       "25%        3903.000000\n",
       "50%        5400.000000\n",
       "75%        7400.000000\n",
       "max       14128.000000\n",
       "Name: MonthlyIncome, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxUpperBound = min([val for (val, out) in zip(df_data.MonthlyIncome, mad_based_outlier(df_data.MonthlyIncome)) if out == True])\n",
    "ind = np.where(df_data.MonthlyIncome>maxUpperBound)\n",
    "df_data.MonthlyIncome[ind[0]] = maxUpperBound\n",
    "ind = np.where(df_data.MonthlyIncome<1500)\n",
    "df_data.MonthlyIncome[ind[0]] = 1500\n",
    "df_data.MonthlyIncome.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NumberOfTimes90DaysLate Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Counter(df_data.NumberOfTimes90DaysLate)\n",
    "### Set outlier values to median , that is 0.\n",
    "ind = np.where(df_data.NumberOfTimes90DaysLate>95)\n",
    "df_data.NumberOfTimes90DaysLate[ind[0]] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NumberRealEstateLoansOrLines Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Counter(df_data.NumberRealEstateLoansOrLines)\n",
    "### Set outlier values to 16.\n",
    "ind = np.where(df_data.NumberRealEstateLoansOrLines>16)\n",
    "df_data.NumberRealEstateLoansOrLines[ind[0]] = 16\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NumberOfTime60-89DaysPastDueNotWorse Variance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Counter(df_data.NumberOfTime6089DaysPastDueNotWorse)\n",
    "### Set outlier values to 0.\n",
    "ind = np.where(df_data.NumberOfTime6089DaysPastDueNotWorse>11)\n",
    "df_data.NumberOfTime6089DaysPastDueNotWorse[ind[0]] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NumberOfDependents Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Counter(df_data.NumberOfDependents)\n",
    "ind = np.where(df_data.NumberOfDependents >10)\n",
    "df_data.NumberOfDependents[ind[0]] = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train-Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "###\n",
    "### Generate Training and Testing Set \n",
    "###\n",
    "    \n",
    "outcome_feature = df_data['SeriousDlqin2yrs']\n",
    "target_features = df_data.drop('SeriousDlqin2yrs', axis=1)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\"\"\"\n",
    "    X_1: independent (target) variables for first data set\n",
    "    Y_1: dependent (outcome) variable for first data set\n",
    "    X_2: independent (target) variables for the second data set\n",
    "    Y_2: dependent (outcome) variable for the second data set\n",
    "\"\"\"\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(target_features, outcome_feature, test_size=0.5, random_state=0)\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Desicion Tree\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight='balanced', criterion='gini', max_depth=6,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-05, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "clf2 = tree.DecisionTreeClassifier(class_weight='balanced',min_impurity_split=1e-05,max_depth=6)\n",
    "clf2.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>50585</td>\n",
       "      <td>19432</td>\n",
       "      <td>70017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>936</td>\n",
       "      <td>4047</td>\n",
       "      <td>4983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>51521</td>\n",
       "      <td>23479</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted    0.0    1.0    All\n",
       "True                          \n",
       "0.0        50585  19432  70017\n",
       "1.0          936   4047   4983\n",
       "All        51521  23479  75000"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = clf2.predict(X_test)\n",
    "pd.crosstab(Y_test, Y_pred, rownames=['True'], colnames=['Predicted'], margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=[0.07, 0.93])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB(priors=[0.07,0.93])\n",
    "gnb.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>56333</td>\n",
       "      <td>13684</td>\n",
       "      <td>70017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>1219</td>\n",
       "      <td>3764</td>\n",
       "      <td>4983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>57552</td>\n",
       "      <td>17448</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted    0.0    1.0    All\n",
       "True                          \n",
       "0.0        56333  13684  70017\n",
       "1.0         1219   3764   4983\n",
       "All        57552  17448  75000"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = gnb.predict(X_test)\n",
    "pd.crosstab(Y_test, Y_pred, rownames=['True'], colnames=['Predicted'], margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=2, cache_size=7000, class_weight={0: 0.1, 1: 0.9}, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True, tol=1,\n",
       "  verbose=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "clf3 = svm.SVC(C=2,cache_size=7000,tol=1,class_weight={0:.1,1:.9} )\n",
    "clf3.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>60033</td>\n",
       "      <td>9984</td>\n",
       "      <td>70017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>3432</td>\n",
       "      <td>1551</td>\n",
       "      <td>4983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>63465</td>\n",
       "      <td>11535</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted    0.0    1.0    All\n",
       "True                          \n",
       "0.0        60033   9984  70017\n",
       "1.0         3432   1551   4983\n",
       "All        63465  11535  75000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = clf3.predict(X_test)\n",
    "pd.crosstab(Y_test, Y_pred, rownames=['True'], colnames=['Predicted'], margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='identity', alpha=0.0001, batch_size='auto',\n",
       "       beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.1, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "clf4 = MLPClassifier(activation=\"identity\", tol= 0.1)\n",
    "clf4.fit(X_train, Y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>68561</td>\n",
       "      <td>1456</td>\n",
       "      <td>70017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>3626</td>\n",
       "      <td>1357</td>\n",
       "      <td>4983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>72187</td>\n",
       "      <td>2813</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted    0.0   1.0    All\n",
       "True                         \n",
       "0.0        68561  1456  70017\n",
       "1.0         3626  1357   4983\n",
       "All        72187  2813  75000"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "Y_pred = clf4.predict(X_test)\n",
    "pd.crosstab(Y_test, Y_pred, rownames=['True'], colnames=['Predicted'], margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=100000.0, class_weight={0: 0.1, 1: 0.9}, dual=False,\n",
       "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
       "          multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
       "          solver='liblinear', tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import  linear_model\n",
    "clf = linear_model.LogisticRegression(C=1e5,class_weight= {0:.1, 1:.9} )\n",
    "clf.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>61853</td>\n",
       "      <td>8164</td>\n",
       "      <td>70017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>1840</td>\n",
       "      <td>3143</td>\n",
       "      <td>4983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>63693</td>\n",
       "      <td>11307</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted    0.0    1.0    All\n",
       "True                          \n",
       "0.0        61853   8164  70017\n",
       "1.0         1840   3143   4983\n",
       "All        63693  11307  75000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = clf.predict(X_test)\n",
    "netMat = (Y_pred == Y_test)\n",
    "clf.coef_\n",
    "ind = np.where(Y_test == 1)\n",
    "Counter(Y_pred[ind])\n",
    "pd.crosstab(Y_test, Y_pred, rownames=['True'], colnames=['Predicted'], margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Small Application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enes 0.154180737 52 5400.0 0.366507841 8 0 0 1 0 0 Decision Tree 0.0\n",
      "Enes 0.154180737 52 5400.0 0.366507841 8 0 0 1 0 0 SVM 0.0\n",
      "Enes 0.154180737 52 5400.0 0.366507841 8 0 0 1 0 0 Logistic Regression 0.0\n",
      "Enes 0.154180737 52 5400.0 0.366507841 8 0 0 1 0 0 GaussianNB 0.0\n",
      "Enes 0.154180737 52 5400.0 0.366507841 8 0 0 1 0 0 MLP 0.0\n",
      "Enes 1.54 52 5400.0 1.06 34 6 9 10 6 6 Decision Tree 1.0\n",
      "Enes 1.54 52 5400.0 1.06 34 6 9 10 6 6 SVM 0.0\n",
      "Enes 1.54 52 5400.0 1.06 34 6 9 10 6 6 Logistic Regression 1.0\n",
      "Enes 1.54 52 5400.0 1.06 34 6 9 10 6 6 GaussianNB 1.0\n",
      "Enes 1.54 52 5400.0 1.06 34 6 9 10 6 6 MLP 1.0\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import *\n",
    "from IPython.display import display\n",
    "import ipywidgets as widgets\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "\n",
    "form_item_layout = widgets.Layout(\n",
    "    display='flex',\n",
    "    flex_flow='row',\n",
    "    justify_content='space-between'\n",
    ")\n",
    "\n",
    "\n",
    "# displaying the text widget\n",
    "text = widgets.Text(\n",
    "    placeholder='Enes',\n",
    "    disabled=False\n",
    ")\n",
    "#display(text)\n",
    "# add button that updates the graph based on the checkboxes\n",
    "button = widgets.Button(description=\"Check credibility\")\n",
    "#display(button)\n",
    "resultLabel = widgets.Label(\n",
    "    value=\"\",\n",
    "    visible = False,\n",
    "    disabled = True\n",
    ")\n",
    "#display(resultLabel)\n",
    "revolve = widgets.FloatSlider(\n",
    "    value=df_data.RevolvingUtilizationOfUnsecuredLines.median(),\n",
    "    min=df_data.RevolvingUtilizationOfUnsecuredLines.min(),\n",
    "    max=df_data.RevolvingUtilizationOfUnsecuredLines.max(),\n",
    "    step=0.01,\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='.4f',\n",
    "    slider_color='black'\n",
    ")\n",
    "age = widgets.IntSlider(\n",
    "    value=df_data.age.median(),\n",
    "    min=df_data.age.min(),\n",
    "    max=df_data.age.max(),\n",
    "    step=1,\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    slider_color='black'\n",
    ")\n",
    "income = widgets.FloatSlider(\n",
    "    value=df_data.MonthlyIncome.median(),\n",
    "    min=df_data.MonthlyIncome.min(),\n",
    "    max=df_data.MonthlyIncome.max(),\n",
    "    step=0.5,\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='.1f',\n",
    "    slider_color='black'\n",
    ")\n",
    "debtRatio = widgets.FloatSlider(\n",
    "    value=df_data.DebtRatio.median(),\n",
    "    min=df_data.DebtRatio.min(),\n",
    "    max=df_data.DebtRatio.max(),\n",
    "    step=0.01,\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='.4f',\n",
    "    slider_color='black'\n",
    ")\n",
    "NumberOfTime3059DaysPastDueNotWorse = widgets.IntSlider(\n",
    "    value=df_data.NumberOfTime3059DaysPastDueNotWorse.median(),\n",
    "    min=df_data.NumberOfTime3059DaysPastDueNotWorse.min(),\n",
    "    max=df_data.NumberOfTime3059DaysPastDueNotWorse.max(),\n",
    "    step=1,\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    slider_color='black'\n",
    ")\n",
    "NumberOfTimes90DaysLate =  widgets.IntSlider(\n",
    "    value=df_data.NumberOfTimes90DaysLate.median(),\n",
    "    min=df_data.NumberOfTimes90DaysLate.min(),\n",
    "    max=df_data.NumberOfTimes90DaysLate.max(),\n",
    "    step=1,\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    slider_color='black'\n",
    ")\n",
    "NumberRealEstateLoansOrLines =  widgets.IntSlider(\n",
    "    value=df_data.NumberRealEstateLoansOrLines.median(),\n",
    "    min=df_data.NumberRealEstateLoansOrLines.min(),\n",
    "    max=df_data.NumberRealEstateLoansOrLines.max(),\n",
    "    step=1,\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    slider_color='black'\n",
    ")\n",
    "NumberOfTime6089DaysPastDueNotWorse =  widgets.IntSlider(\n",
    "    value=df_data.NumberOfTime6089DaysPastDueNotWorse.median(),\n",
    "    min=df_data.NumberOfTime6089DaysPastDueNotWorse.min(),\n",
    "    max=df_data.NumberOfTime6089DaysPastDueNotWorse.max(),\n",
    "    step=1,\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    slider_color='black'\n",
    ")\n",
    "NumberOfOpenCreditLinesAndLoans = widgets.IntSlider(\n",
    "    value=df_data.NumberOfOpenCreditLinesAndLoans.median(),\n",
    "    min=df_data.NumberOfOpenCreditLinesAndLoans.min(),\n",
    "    max=df_data.NumberOfOpenCreditLinesAndLoans.max(),\n",
    "    step=1,\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    slider_color='black'\n",
    ")\n",
    "\n",
    "NumberOfDependents =  widgets.IntSlider(\n",
    "    value=df_data.NumberOfDependents.median(),\n",
    "    min=df_data.NumberOfDependents.min(),\n",
    "    max=df_data.NumberOfDependents.max(),\n",
    "    step=1,\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    slider_color='black'\n",
    ")\n",
    "algos = widgets.Dropdown(options=['Decision Tree', 'SVM', 'Logistic Regression','GaussianNB','MLP'])\n",
    "#display(revolve)\n",
    "\n",
    "form_items = [\n",
    "   Box([Label(value='Please, enter the name:'), text], layout=form_item_layout),\n",
    "   Box([Label(value='Revolving Util. of Unsecured Lines:'), revolve], layout=form_item_layout),\n",
    "   Box([Label(value='Age:'), age], layout=form_item_layout),\n",
    "   Box([Label(value='Monthly Income:'), income], layout=form_item_layout),\n",
    "   Box([Label(value='Dept Ratio:'), debtRatio], layout=form_item_layout),\n",
    "   Box([Label(value='NumberOfOpenCreditLinesAndLoans:'), NumberOfOpenCreditLinesAndLoans], layout=form_item_layout),\n",
    "   Box([Label(value='NumberOfTime3059DaysPastDueNotWorse:'), NumberOfTime3059DaysPastDueNotWorse], layout=form_item_layout),\n",
    "   Box([Label(value='NumberOfTimes90DaysLate:'), NumberOfTimes90DaysLate], layout=form_item_layout),\n",
    "   Box([Label(value='NumberRealEstateLoansOrLines:'), NumberRealEstateLoansOrLines], layout=form_item_layout),\n",
    "   Box([Label(value='NumberOfTime6089DaysPastDueNotWorse:'), NumberOfTime6089DaysPastDueNotWorse], layout=form_item_layout),\n",
    "   Box([Label(value='NumberOfDependents:'), NumberOfDependents], layout=form_item_layout),\n",
    "   Box([Label(value='Algorithm:'),algos], layout=form_item_layout),\n",
    "   button,\n",
    "   Box([Label(value='Result:'), resultLabel], layout=form_item_layout),\n",
    "]\n",
    "\n",
    "form = Box(form_items, layout=Layout(\n",
    "    display='flex',\n",
    "    flex_flow='column',\n",
    "    border='dashed 2px',\n",
    "    align_items='stretch',\n",
    "    width= '70%'\n",
    "))\n",
    "\n",
    "display(form)\n",
    "\n",
    "# function to deal with the checkbox update button       \n",
    "def on_button_clicked(b):\n",
    "    name = text.value\n",
    "    roul = revolve.value\n",
    "    ageV = age.value\n",
    "    monI = income.value\n",
    "    dratio = debtRatio.value\n",
    "    noOpCL = NumberOfOpenCreditLinesAndLoans.value\n",
    "    noT3059 = NumberOfTime3059DaysPastDueNotWorse.value\n",
    "    noT90 = NumberOfTimes90DaysLate.value\n",
    "    nreL = NumberRealEstateLoansOrLines.value\n",
    "    noT6089 = NumberOfTime6089DaysPastDueNotWorse.value\n",
    "    noD = NumberOfDependents.value\n",
    "    algo = algos.value\n",
    "    \n",
    "    testARR = [[dratio,monI,noD,noOpCL,noT3059,noT6089,noT90,nreL,roul,ageV]]\n",
    "    yGuess  = []\n",
    "    \n",
    "    if   algo == 'Decision Tree':\n",
    "        yGuess = clf2.predict(testARR)\n",
    "    elif algo == 'SVM':\n",
    "        yGuess = clf3.predict(testARR)\n",
    "    elif algo == 'Logistic Regression':\n",
    "        yGuess = clf.predict(testARR)\n",
    "    elif algo == 'GaussianNB':\n",
    "        yGuess = gnb.predict(testARR)\n",
    "    else: \n",
    "        yGuess = clf4.predict(testARR)\n",
    "    \n",
    "    print(name,roul,ageV,monI,dratio,noOpCL,noT3059,noT90,nreL,noT6089,noD,algo,yGuess[0])\n",
    "    \n",
    "    if(resultLabel.visible == False and yGuess[0] == 0):\n",
    "        resultLabel.value = text.value + ' can be provided with the loan.'    \n",
    "    else:\n",
    "        resultLabel.value = text.value + ' should not be provided with the loan.'\n",
    "        \n",
    "button.on_click(on_button_clicked)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2c4e25fc908>,\n",
       " <matplotlib.lines.Line2D at 0x2c4d7881588>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9N8lKN0otFQ0LS+JuX6QQAAAAAGCWcrns2j41NeXaHjWJS8ooBAAAAACYJZ1Ou7ZnMpmA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\nQIhIygAAAAAgRCRlAAAAABCi/x/ntedCahTJpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c4e2f0b668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind = df_data.SeriousDlqin2yrs[df_data.SeriousDlqin2yrs == 1]\n",
    "ind = ind[1:100]\n",
    "ind2 = df_data.SeriousDlqin2yrs[df_data.SeriousDlqin2yrs == 0]\n",
    "ind2 = ind2[1:100]\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(15,15))\n",
    "ax = plt.subplot(211)\n",
    "#ax.set_ylim(0,20)\n",
    "plt.plot(df_data.MonthlyIncome[ind.index],df_data.age[ind.index], 'ko',df_data.MonthlyIncome[ind2.index],df_data.age[ind2.index], 'go')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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33I/V20v6FYUqfENEpU9EREREZBRq7fSzel0ZX5e1EB/j5Yb5eRybmzho++va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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c4d8516518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "fpr, tpr, threshold = roc_curve(Y_test, Y_pred)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()\n"
   ]
  }
 ],
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